LLM-as-Judge: Designing Reliable Automated Evaluation (EXPLAINED)
Hard project question on LLM-as-judge — rubric design, position bias, human calibration, and production eval pipelines.
TL;DR — Quick Answer
LLM-as-judge uses a strong model to score outputs against a rubric (correctness, faithfulness, tone, safety). Reliability requires: clear scoring criteria with anchored examples, blind comparison (hide model identity), mitigation of position/length/self-preference bias, calibration against human labels (correlation targets), ensemble judges or multi-sample scoring, and golden sets with adversarial cases. Do not trust judges for high-stakes legal/medical decisions without human audit; use judges for scale, humans for ground truth.
The Interview Question
How do you design a reliable LLM-as-judge evaluation pipeline? Cover bias, calibration, rubrics, and when not to trust automated judges.
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